Injuries caused by falling impact many people today, especially the elderly. The resulting hospitalizations are responsible for a significant resource cost to health care systems. Though there are factors tied to a greater risk of falls, these factors are not easily measured and assessed for each individual. Further, fall tracking is not commonplace, and most falls are self-reported by the patient to a physician, if reported at all. Individuals with neurological disorders such as Parkinson's disease and multiple sclerosis have a greater risk of falling because symptoms of these diseases have a toll on their central nervous systems and motor systems.
Existing systems can detect when an individual falls, and seek assistance for the individual. However, these systems are reactive measures, and a proactive solution currently does not exist. Further, existing systems do not generate recommendations regarding how an individual can reduce their risk of falling. Not only is it important to detect the fall, but also to monitor an individual's behavior on a regular basis to assess their fall risk over time and predict when they may be more vulnerable to falling. Since many elderly individuals live on their own, it is important for others (e.g., caretaker and family members) to have a way to remotely monitor the well-being of the individual and respond with appropriate help if the individual has fallen, which current systems do not provide.